5.17 intro to numpy and arrays

In [1]:

    

my_list = [1,2,3] # list


    

In [2]:

    

my_list


    

    
Out[2]:





[1, 2, 3]

In [3]:

    

import numpy as np


    

In [4]:

    

np.array(my_list)


    

    
Out[4]:





array([1, 2, 3])

In [5]:

    

my_lol = [[1,2,3],[4,5,6],[7,8,9]] # list of lists


    

In [7]:

    

np.array(my_lol) # cast lol as 2d array. 2d == 2 levels of brackets


    

    
Out[7]:





array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

In [10]:

    

np.arange(0,10,2) # (arange == 'array range')


    

    
Out[10]:





array([0, 2, 4, 6, 8])

In [20]:

    

np.zeros(3) # 1d matrix of zeros


    

    
Out[20]:





array([ 0.,  0.,  0.])

In [21]:

    

np.zeros((5,5)) # 2d matrix of zeros, size defined by tuple


    

    
Out[21]:





array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

In [15]:

    

np.ones((2,4))


    

    
Out[15]:





array([[ 1.,  1.,  1.,  1.],
       [ 1.,  1.,  1.,  1.]])

In [18]:

    

np.linspace(0,5,10) # 0-5 in ten equal steps. one-dimensional because only one bracket.


    

    
Out[18]:





array([ 0.        ,  0.55555556,  1.11111111,  1.66666667,  2.22222222,
        2.77777778,  3.33333333,  3.88888889,  4.44444444,  5.        ])

In [25]:

    

np.eye(6) # identity matrix, square array with diagonal streak of ones; useful in linear algebra


    

    
Out[25]:





array([[ 1.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  1.]])

In [30]:

    

np.random.rand(8) # random, evenly distributed values between 0 and 1


    

    
Out[30]:





array([ 0.94281769,  0.07352679,  0.67758591,  0.90855607,  0.62023528,
        0.0184851 ,  0.25607745,  0.47223303])

In [29]:

    

np.random.rand(5,5) # note that this does not take a tuple


    

    
Out[29]:





array([[ 0.21296376,  0.34214548,  0.06525983,  0.50073381,  0.00949134],
       [ 0.02751488,  0.74050259,  0.68150845,  0.38250846,  0.53251133],
       [ 0.01213005,  0.20244446,  0.86817604,  0.97044167,  0.37523837],
       [ 0.78798428,  0.51939935,  0.05645766,  0.74499824,  0.7892101 ],
       [ 0.39495495,  0.57984132,  0.27374584,  0.53635004,  0.20940844]])

In [32]:

    

np.random.randn(5,5) # random, normally-distributed values, distribution centered on 0


    

    
Out[32]:





array([[-1.91203663, -0.52787125,  1.36753625, -0.90693989,  0.38105558],
       [ 0.56568902, -1.28475963, -0.53472699,  0.91658542,  0.3049844 ],
       [ 0.27162795, -0.40690948,  1.30689999,  1.4499117 ,  0.39435586],
       [-0.01567394,  0.90361041, -0.94111586, -0.74511823,  1.95083454],
       [ 1.3815438 ,  0.77528303, -1.30183163,  0.13992134, -0.43431779]])

In [60]:

    

np.random.randint(100) # I think, in py2.7, low is 0 by default, high is input.


    

    
Out[60]:





87

In [45]:

    

np.random.randint(3,9,(3,3)) # random value between low (inclusive) and high (exclusive). eg: 3 <= value < 9


    

    
Out[45]:





array([[7, 3, 6],
       [6, 4, 4],
       [4, 3, 8]])

In [61]:

    

arr = np.arange(25)


    

In [62]:

    

ranarr = np.random.randint(0,50,10)


    

In [74]:

    

arr = arr.reshape(5,5) 
arr


    

    
Out[74]:





array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19],
       [20, 21, 22, 23, 24]])

In [64]:

    

ranarr.max()


    

    
Out[64]:





49

In [67]:

    

ranarr.min()


    

    
Out[67]:





10

In [68]:

    

ranarr.argmax()


    

    
Out[68]:





4

In [70]:

    

ranarr.argmin()


    

    
Out[70]:





8

In [75]:

    

arr.shape


    

    
Out[75]:





(5, 5)

In [72]:

    

ranarr.shape


    

    
Out[72]:





(10,)

In [77]:

    

arr.dtype


    

    
Out[77]:





dtype('int64')

In [78]:

    

ranarr.dtype


    

    
Out[78]:





dtype('int64')

In [80]:

    

from numpy.random import randint


    

In [81]:

    

randint(2,13)


    

    
Out[81]:





8

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